A method, device, equipment and medium for optimizing the collaborative deployment of multi-domain computing resources

Abstract

The present invention belongs to the field of communication technology, and specifically relates to a method, device, equipment and medium for optimizing the collaborative deployment of multi-domain computing resources. The method includes determining the communication delay of a service from an edge substation to the cloud, constructing a first objective function with the goal of maximizing delay redundancy, and solving to obtain the shortest service communication delay. The service quality function and the resource abundance index are calculated based on the shortest service communication delay. When the resource abundance does not meet the preset conditions, a second objective function with the goal of minimizing the investment cost of computing resources is constructed, and the optimal solution for the collaborative deployment of multi-domain computing resources is solved. This solution abandons the traditional service hierarchical transmission method, calculates the shortest communication delay of cloud-edge services through the maximum redundant delay, evaluates resource abundance, and provides a low-cost resource collaborative deployment solution.

Description

Technical Field

[0001] The present invention belongs to the field of communication technology, and specifically relates to a method, device, equipment and medium for collaborative deployment optimization of multi-domain computing resources. Background Art

[0002] The application of edge substation and cloud master station technologies can effectively enhance the intelligence level of new power distribution systems and meet complex and ever-changing operational requirements. In new power distribution systems, edge substations enable real-time processing and analysis of massive amounts of data. As key nodes in the new distribution system, they undertake tasks such as data acquisition, preprocessing, and real-time control. The cloud master station provides comprehensive data support for the operational control of the new distribution system. Consequently, a close collaborative working relationship is established between the cloud master station and edge substations. The edge substations are responsible for collecting and processing real-time data and uploading key data to the cloud master station for further analysis and mining. The cloud master station, based on the data uploaded by the edge substations, performs global optimization and scheduling, achieving precise control of the entire power system.

[0003] While current cloud-edge transmission systems in distribution networks offer numerous advantages, they also have significant drawbacks. While edge computing can alleviate bandwidth pressure on cloud master stations, in some cases, the bandwidth from edge substations to the cloud master is limited, making it unable to meet the demands of high-concurrency, multi-service operations. Traditional approaches typically employ hierarchical transmission of services, resulting in a certain degree of resource waste and failing to fully exploit the limits of existing resource clusters. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, device, equipment and medium for collaborative deployment optimization of multi-domain computing resources to solve the problems in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for optimizing the collaborative deployment of multi-domain computing resources, comprising the following steps:

[0007] Determine the communication latency of services transmitted from edge substations to the cloud;

[0008] Based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as a first constraint of the first objective function; the first objective function is solved based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy;

[0009] Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions;

[0010] When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein, the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0011] Furthermore, the communication latency of services transmitted from the edge substation to the cloud is determined, including:

[0012] The communication delay of the service at the edge substation includes the unloading delay of the service at the edge substation, the transmission delay from the edge substation to the cloud master station, and the unloading delay of the service at the cloud master station.

[0013] Furthermore, the service unloading delay at the edge substation , Transmission delay from edge substation to cloud master station and the service offloading delay at the cloud master station , respectively expressed as follows:

[0014]

[0015]

[0016]

[0017] in, It is at the edge station The amount of data that the service offloads locally; It is an edge substation The amount of data transmitted from the business to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing power resources for the edge; Assign edge substations to edge terminals The percentage of computing power resources in the total computing power resources of the edge; Cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; For cloud computing resources; Distributing processing edge substations to the cloud The percentage of computing resources used for transmission services in the total computing resources of the cloud.

[0018] Furthermore, based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as a first constraint condition of the first objective function;

[0019] Wherein, the first objective function is expressed as:

[0020]

[0021] The first constraint is expressed as:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028] in, They respectively represent the amount of data offloaded locally by the business, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud to the total cloud computing resources. is the number of edge substations at the edge; For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; Edge substation Communication delay of the service; It is an edge substation The amount of business data; It is at the edge station The amount of data that the service offloads locally; Assign edge substations to edge terminals The percentage of computing power resources in the total computing power resources of the edge; Distributing processing edge substations to the cloud The percentage of computing resources used for transmission services in the total computing resources of the cloud; Assigned to edge substations The percentage of the transmission bandwidth to the cloud-edge transmission bandwidth.

[0029] Furthermore, the general computing resource collaborative deployment optimization model includes a second objective function with the goal of minimizing the general computing resource investment cost, and a second constraint condition with the expected resource abundance as a constraint;

[0030] Wherein, the second objective function is expressed as:

[0031]

[0032] The second constraint is expressed as:

[0033]

[0034]

[0035]

[0036] in, They represent the number of bits of data that can be transmitted per second and the number of computer cycles per second respectively; Indicates expected resource sufficiency; To calculate the total resource investment cost.

[0037] Furthermore, the total resource investment cost is calculated Expressed as:

[0038]

[0039] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; Both are factors affecting computing resource costs; The upper limit of the communication technology bandwidth; The maximum computing power supported by a single device; 、 They are respectively the deployment cost of communication resources when the upper limit of the communication technology bandwidth is not exceeded and the deployment cost of communication resources when the upper limit of the communication technology bandwidth is exceeded; 、 The deployment costs of computing resources are as follows: when the computing power does not exceed the maximum support capability of a single device, and when the computing power exceeds the maximum support capability of a single device. They represent the number of bits of data that can be transmitted per second and the number of computer cycles per second respectively.

[0040] Furthermore, in the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

[0041] A second aspect of the present invention provides a multi-domain computing resource collaborative deployment optimization device, comprising:

[0042] The latency determination module is used to determine the communication latency of services transmitted from the edge substation to the cloud;

[0043] A first optimization module is configured to construct a first objective function based on the communication delay and with the goal of maximizing delay redundancy, and use the communication and computing resources allocated to each edge substation as a first constraint of the first objective function; solve the first objective function based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy;

[0044] A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions;

[0045] The second optimization module is used to construct a collaborative deployment optimization model for computing resources based on the shortest business communication delay when the cloud-edge resource abundance index does not meet the preset conditions; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0046] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the multi-domain computing resource collaborative deployment optimization method as described above.

[0047] In a fourth aspect, the present invention provides a computer-readable storage medium storing at least one instruction that, when executed by a processor, implements the aforementioned multi-domain computing resource collaborative deployment optimization method. Compared with the prior art, the present invention has the following beneficial effects:

[0048] The multi-domain computing resource collaborative deployment optimization method provided by the present invention is aimed at the high-concurrency differentiated spatiotemporal business flow requirements of cloud-edge, abandons the traditional method of hierarchical business transmission, calculates the shortest communication delay of cloud-edge business by constructing and solving the maximum redundant delay, and uses this to evaluate the cloud-edge resource abundance, and provides a low-cost multi-domain resource collaborative deployment solution, which explores the potential of the original resource cluster, makes the resource deployment in this scenario more reasonable, realizes the reasonable control of the investment scale, and can maximize the reuse efficiency of computing resources. Under the requirement of ensuring resource abundance, it has important guiding significance for the allocation of related computing resources in the new distribution network with the goal of minimizing investment costs. The multi-domain computing resource collaborative deployment optimization device, electronic device and computer-readable storage medium provided by the present invention also solve the problems raised in the background technology part. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 A flowchart of a multi-domain computing resource collaborative deployment optimization method according to an embodiment of the present invention;

[0051] Figure 2 This is a scenario diagram of the transmission of multiple high-concurrency services from an edge substation to a cloud master server in an embodiment of the present invention;

[0052] Figure 3 This is a structural block diagram of a multi-domain computing resource collaborative deployment optimization device according to an embodiment of the present invention;

[0053] Figure 4 The figure is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, unless there is a conflict, the embodiments and features in the embodiments of the present application can be combined with each other.

[0055] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0056] Example 1

[0057] An embodiment of the present invention provides a multi-domain computing resource collaborative deployment optimization method that is adapted to distributed driving of high-concurrency differentiated spatiotemporal business flows. The method includes: calculating the communication delay of the business transmitted from the edge substation to the cloud main station, which is composed of the unloading delay of the business at the edge substation, the transmission delay from the edge substation to the cloud main station, and the unloading delay of the business at the cloud main station; constructing a delay redundancy function, calculating the maximum delay redundancy of cloud-edge business transmission, and finding the shortest business communication delay under the maximum delay redundancy; calculating the service quality function of the business transmitted from each edge substation to the cloud main station, calculating the cloud-edge resource abundance index, and constructing a resource abundance evaluation model; when the resource abundance is less than 1, communication and computing power resources need to be redeployed, and with the expected resource abundance as a constraint, a computing resource collaborative deployment optimization model that minimizes the investment cost of computing resources is constructed, and this optimization problem is solved by the CVX toolbox in MATLAB.

[0058] like Figure 1 As shown, a multi-domain computing resource collaborative deployment optimization method includes the following steps:

[0059] S1. Determine the communication delay of the service from the edge substation to the cloud.

[0060] like Figure 2 As shown, in this solution, a scenario in which multiple high-concurrency services are transmitted from an edge substation to a cloud master server may include a cloud master and several edge substations, and services are transmitted from the edge substations to the cloud master.

[0061] Specifically, edge substations i Communication delay of the service Including the service unloading delay at the edge substation , Transmission delay from edge substation to cloud master station and the service offloading delay at the cloud master station , which is expressed as follows:

[0062]

[0063] More specifically, the service unloading delay at the edge substation , Transmission delay from edge substation to cloud master station and the service offloading delay at the cloud master station , respectively expressed as follows:

[0064]

[0065]

[0066]

[0067]

[0068] in, It is an edge substation The amount of business data; It is at the edge station The amount of data that the service offloads locally; It is an edge substation The amount of data transmitted from the business to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing power resources for the edge; Assign edge substations to edge terminals The percentage of computing power resources in the total computing power resources of the edge; Cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; For cloud computing resources; Distributing processing edge substations to the cloud The percentage of computing resources used for transmission services in the total computing resources of the cloud.

[0069] S2. Based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as the first constraint condition of the first objective function; based on the first constraint condition, the first objective function is solved to obtain the shortest service communication delay under maximum delay redundancy.

[0070] Wherein, the first objective function is expressed as:

[0071]

[0072] The first constraint is expressed as:

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079] in, They respectively represent the amount of data offloaded locally by the business, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud to the total cloud computing resources. is the number of edge substations at the edge; For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; Edge substation Communication delay of the service; It is an edge substation The amount of business data; It is at the edge station The amount of data that the service offloads locally; Assign edge substations to edge terminals The percentage of computing power resources in the total computing power resources of the edge; Distributing processing edge substations to the cloud The percentage of computing resources used for transmission services in the total computing resources of the cloud; Assigned to edge substations The percentage of the transmission bandwidth to the cloud-edge transmission bandwidth.

[0080] It should be noted that the shortest service transmission delay refers to the minimum time required for the service to be transmitted from the edge substation to the cloud main station under given resource allocation and constraints. After the shortest service transmission delay is determined, the communication delay Then it was determined.

[0081] S3. Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions.

[0082] The calculation formula is as follows:

[0083]

[0084] in, Edge substation Quality of service function; is the resource abundance index.

[0085] S4. When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein, the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0086] Specifically, when the resource abundance index is less than 1, general computing resources are redeployed. In this solution, the expected resource abundance is set to 1.

[0087] Wherein, the second objective function is expressed as:

[0088]

[0089] The second constraint is expressed as:

[0090]

[0091]

[0092]

[0093] in, They represent the number of bits of data that can be transmitted per second and the number of computer cycles per second respectively; Indicates expected resource sufficiency; To calculate the total resource investment cost.

[0094] More specifically, calculate the resource investment cost Expressed as:

[0095]

[0096] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; Both are factors affecting computing resource costs; The upper limit of the communication technology bandwidth; The maximum computing power supported by a single device; 、 They are respectively the deployment cost of communication resources when the upper limit of the communication technology bandwidth is not exceeded and the deployment cost of communication resources when the upper limit of the communication technology bandwidth is exceeded; 、 The deployment costs of computing resources are as follows: when the computing power does not exceed the maximum support capability of a single device, and when the computing power exceeds the maximum support capability of a single device. They represent the number of bits of data that can be transmitted per second and the number of computer cycles per second respectively.

[0097] Specifically, in the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

[0098] In another optional embodiment, a method for optimizing the collaborative deployment of multi-domain computing resources adapted to distributed drive of high-concurrency differentiated spatiotemporal business flows is provided, the method comprising the following steps:

[0099] Step 1: Calculate the communication delay of the service from the edge substation to the cloud. The communication delay of the service is composed of the service unloading delay at the edge substation, the transmission delay from the edge substation to the cloud master station, and the service unloading delay at the cloud master station.

[0100] Step 2: Construct a delay redundancy function, use the communication and computing resources allocated to each edge substation as constraints, calculate the maximum cloud-edge delay redundancy, and find the shortest service transmission delay under the maximum delay redundancy.

[0101] Step 3: Calculate the service quality function of the business transmitted from each edge substation to the cloud main station, calculate the cloud-edge resource sufficiency index, and build a resource sufficiency evaluation model.

[0102] Step 4: Determine whether to redeploy computing resources based on the resource sufficiency index. When the resource sufficiency index is less than 1, the transmission quality of services from the edge substation to the cloud master station is poor, and communication and computing resources need to be redeployed.

[0103] Step 5: Taking the expected resource abundance as a constraint, a computing resource collaborative deployment optimization model that minimizes the computing resource investment cost is constructed. This optimization problem is solved by the CVX toolbox in MATLAB.

[0104] In step 1, the communication delay of services transmitted from the edge substation to the cloud master station in the new distribution network is calculated, including:

[0105] Edge substation For example, due to the constraints of communication resources and computing resources, the edge substation Communication delay of the service Delay of service unloading at edge substation , Transmission delay from edge substation to cloud master station and the service offloading delay at the cloud master station The calculation formula is as follows:

[0106]

[0107] in It is an edge substation The amount of business data; It is at the edge station The amount of data that the service offloads locally; It is an edge substation The amount of data transmitted from the business to the cloud master station; It is the number of CPU cycles required to process a unit bit of data; The computing power resources for the edge; Assign edge substations to edge terminals The percentage of computing power resources in the total computing power resources of the edge; Cloud-edge transmission bandwidth; Assigned to edge substations The percentage of transmission bandwidth to cloud-edge transmission bandwidth; For cloud computing resources; Distributing processing edge substations to the cloud The percentage of computing resources used for transmission services in the total computing resources of the cloud;

[0108] In step 2, a delay redundancy function is constructed, and the maximum cloud-edge delay redundancy is calculated based on the communication and computing resources allocated to each edge substation.

[0109] The calculation formula is as follows:

[0110]

[0111] in, For services under computing resource constraints, The maximum delay for successful transmission to the cloud master station; is the number of edge substations at the edge;

[0112] In step 2, the maximum delay redundancy can be solved in a given feasible domain using the CXV toolbox in MATLAB.

[0113] In step 3, the edge substation For example, we calculate the service quality function of the business transmitted from each edge substation to the cloud master station, calculate the cloud-edge resource sufficiency index, and build a resource sufficiency evaluation model. The calculation formula is as follows:

[0114]

[0115] in, Edge substation Quality of service function; is the resource abundance index.

[0116] In step 4, it is determined whether it is necessary to redeploy the computing resources based on the resource abundance index. When , the general computing resource deployment meets the transmission delay requirements of all business data packets, and there is no need to deploy general computing resources. When the service is transmitted from the edge substation to the cloud main station, the transmission quality is poor, and communication and computing resources need to be redeployed.

[0117] when When , communication and computing resources are redeployed. Taking the expected resource abundance as a constraint, a computing resource collaborative deployment optimization model that minimizes the computing resource investment cost is constructed. This optimization problem is solved by the CVX toolbox in MATLAB, and the calculation formula is:

[0118] Let the total cost of resource deployment be :

[0119]

[0120] in, They are the communication resource deployment cost and computing resource deployment cost respectively; All are factors affecting communication resource costs; Both are factors affecting computing resource costs; The upper limit of the communication technology bandwidth; The maximum computing power supported by a single device.

[0121] Solve the minimum cloud-edge computing resource deployment cost in a given feasible domain. The calculation formula is as follows:

[0122]

[0123] In the simulation environment, the total communication latency of the service is determined. Based on the corresponding quality of service function, the resource sufficiency of the cloud-edge architecture is further calculated. Based on the resource sufficiency index, whether to reallocate communication and computing resources is determined. When the resource sufficiency index is less than 1, computing resources are redeployed. With the goal of minimizing deployment costs and maximizing resource sufficiency, a corresponding linear programming problem is formulated and solved to optimize the deployment scale of communication and computing resources.

[0124] Example 2

[0125] like Figure 3 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a multi-domain computing resource collaborative deployment optimization device, including:

[0126] The latency determination module is used to determine the communication latency of services transmitted from the edge substation to the cloud;

[0127] A first optimization module is configured to construct a first objective function based on the communication delay and with the goal of maximizing delay redundancy, and use the communication and computing resources allocated to each edge substation as a first constraint of the first objective function; solve the first objective function based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy;

[0128] A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions;

[0129] The second optimization module is used to construct a collaborative deployment optimization model for computing resources based on the shortest business communication delay when the cloud-edge resource abundance index does not meet the preset conditions; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0130] Example 3

[0131] like Figure 4 As shown, the present invention also provides an electronic device 100 for implementing a multi-domain computing resource collaborative deployment optimization method;

[0132] The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0133] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a multi-domain computing resource collaborative deployment optimization method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0134] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device.

[0135] The at least one processor 102 may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor. The processor 102 is the control center of the electronic device 100 and connects various parts of the entire electronic device 100 using various interfaces and lines.

[0136] The memory 101 in the electronic device 100 stores multiple instructions to implement a method for optimizing the collaborative deployment of multi-domain computing resources. The processor 102 can execute the multiple instructions to implement:

[0137] Determine the communication latency of services transmitted from edge substations to the cloud;

[0138] Based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as a first constraint of the first objective function; the first objective function is solved based on the first constraint to obtain the shortest service communication delay under the maximum delay redundancy;

[0139] Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions;

[0140] When the cloud-edge resource abundance index does not meet the preset conditions, a collaborative deployment optimization model for computing resources is constructed based on the shortest service communication delay; wherein, the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources, and a second constraint condition with the expected resource abundance as a constraint; the second objective function is solved based on the second constraint condition to obtain the optimal solution for the collaborative deployment of multi-domain computing resources.

[0141] Example 4

[0142] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. Computer-readable media may include: any entity or device that can carry computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0143] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0144] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0145] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0146] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0147] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the collaborative deployment of multi-domain computing resources, characterized in that: The following steps are involved: Determine the communication latency of services transmitted from edge substations to the cloud; Based on the communication delay, a first objective function is constructed with the goal of maximizing delay redundancy, and the communication and computing resources allocated to each edge substation are used as a first constraint condition of the first objective function; Solving the first objective function based on the first constraint condition to obtain the shortest service communication delay under the maximum delay redundancy; Wherein, the first objective function is expressed as: The first constraint is expressed as: C1:0≤D i,e ≤D i C2:0<λ i,e <1 C3:0<λ i,c <1 C4:0<γ i <1 Among them, D e ,λ e ,γ,λ c They represent the amount of data offloaded locally, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud to the total cloud computing resources. N is the number of edge substations at the edge. T i is the maximum delay for the service to be successfully transmitted from the edge substation i to the cloud master station under the constraints of the general computing resources; t i D is the communication delay of the service at edge substation i; i is the amount of business data on edge substation i; D i,e is the amount of data that the service offloads locally at the edge substation i; i,e is the percentage of computing power resources allocated to edge substation i at the edge to the total computing power resources at the edge; i,c The percentage of computing power resources allocated to the cloud to process the transmission business of edge substation i in the total computing power resources of the cloud; γ i is the percentage of the transmission bandwidth allocated to edge substation i in the cloud-edge transmission bandwidth; Calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; calculate the cloud-edge resource abundance index based on each of the service quality functions; When the cloud-edge resource sufficiency index does not meet the preset conditions, a computing resource collaborative deployment optimization model is constructed based on the shortest service communication delay; wherein the computing resource collaborative deployment optimization model includes a second objective function with the goal of minimizing the computing resource investment cost and a second constraint condition with the expected resource sufficiency as a constraint; the second objective function is solved based on the second constraint condition to obtain an optimal solution for the multi-domain computing resource collaborative deployment; Wherein, the second objective function is expressed as: The second constraint is expressed as: C1:x>0 C2:l≥0 C3:λ=1 Here, x and l represent the number of bits of data that can be transmitted per second and the number of computer cycles per second, respectively; λ represents the expected resource abundance; and z is the total resource investment cost.

2. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: Determine the communication latency of services transmitted from edge substations to the cloud, including: The communication delay of the service at the edge substation includes the unloading delay of the service at the edge substation, the transmission delay from the edge substation to the cloud master station, and the unloading delay of the service at the cloud master station.

3. The multi-domain computing resource collaborative deployment optimization method according to claim 2 is characterized in that: Service unloading delay at the edge substation t i,e , the transmission delay from the edge substation to the cloud master station t i,tr and the service unloading delay t at the cloud master station i,c , respectively expressed as follows: Among them, D i,e D is the amount of data that the service offloads locally at the edge substation i; i,c is the amount of data transmitted from the edge substation i to the cloud master station; f i is the number of CPU cycles required to process a unit bit of data; F e is the computing power resource at the edge; i,e is the percentage of computing power resources allocated to edge substation i by the edge to the total computing power resources of the edge; B is the cloud-edge transmission bandwidth; γ i is the percentage of the transmission bandwidth allocated to edge substation i in the cloud-edge transmission bandwidth; F c is the computing power resource in the cloud; i,c The percentage of computing power resources allocated to the cloud to process the transmission business of edge substation i as a percentage of the total computing power resources of the cloud.

4. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: The total resource investment cost z is expressed as: z=z tx +with sl Among them, z tx ,z sl They are the communication resource deployment cost and computing resource deployment cost respectively; α and β are both factors affecting the communication resource cost; μ and ν are both factors affecting the computing resource cost; x0 is the upper limit of the communication technology bandwidth; l0 is the maximum computing power support capability of a single device; x α 、x β are the deployment cost of communication resources when the bandwidth upper limit of the communication technology is not exceeded and the deployment cost of communication resources when the bandwidth upper limit of the communication technology is exceeded; l μ 、l v They are respectively the computing resource deployment cost when the computing power support capability of a single device is not exceeded and the computing resource deployment cost when the computing power support capability of a single device is exceeded; x and l represent the number of bits of data that can be transmitted per second and the number of computer operation cycles per second, respectively.

5. The multi-domain computing resource collaborative deployment optimization method according to claim 1 is characterized in that: In the step of solving the second objective function based on the second constraint condition, the CVX toolbox in MATLAB is used for solving.

6. A multi-domain computing resource collaborative deployment optimization device, characterized in that: include: The latency determination module is used to determine the communication latency of services transmitted from the edge substation to the cloud; A first optimization module is configured to construct a first objective function based on the communication delay and with the goal of maximizing delay redundancy, and use the communication and computing resources allocated to each edge substation as a first constraint condition of the first objective function; Solving the first objective function based on the first constraint condition to obtain the shortest service communication delay under the maximum delay redundancy; Wherein, the first objective function is expressed as: The first constraint is expressed as: C1:0≤D i,e ≤D i C2:0<λ i,e <1 C3:0<λ i,c <1 C4:0<γ i <1 Among them, D e ,λ e ,γ,λ c They represent the amount of data offloaded locally, the percentage of edge substation computing resources in the total edge computing resources, the percentage of bandwidth allocated to edge substations in the total transmission bandwidth, and the percentage of computing resources allocated to edge substations in the cloud to the total cloud computing resources. N is the number of edge substations at the edge. T i is the maximum delay for the service to be successfully transmitted from the edge substation i to the cloud master station under the constraints of the general computing resources; t i D is the communication delay of the service at edge substation i; i is the amount of business data on edge substation i; D i,e is the amount of data that the service offloads locally at the edge substation i; i,e is the percentage of computing power resources allocated to edge substation i at the edge to the total computing power resources at the edge; i,c The percentage of computing power resources allocated to the cloud to process the transmission business of edge substation i in the total computing power resources of the cloud; γ i is the percentage of the transmission bandwidth allocated to edge substation i in the cloud-edge transmission bandwidth; A sufficiency calculation module is used to calculate the service quality function of the service transmitted from each edge substation to the cloud master station based on the shortest service communication delay; and calculate the cloud-edge resource sufficiency index based on each of the service quality functions; A second optimization module is configured to construct a collaborative deployment optimization model for computing resources based on the shortest service communication delay when the cloud-edge resource sufficiency index does not meet a preset condition; wherein the collaborative deployment optimization model for computing resources includes a second objective function with the goal of minimizing the investment cost of computing resources and a second constraint condition with the expected resource sufficiency as a constraint; and solve the second objective function based on the second constraint condition to obtain an optimal solution for the collaborative deployment of multi-domain computing resources; Wherein, the second objective function is expressed as: The second constraint is expressed as: C1:x>0 C2:l≥0 C3:λ=1 Here, x and l represent the number of bits of data that can be transmitted per second and the number of computer cycles per second, respectively; λ represents the expected resource abundance; and z is the total resource investment cost.

7. An electronic device, characterized in that: It includes a processor and a memory, and the processor is used to execute the computer program stored in the memory to implement the multi-domain computing resource collaborative deployment optimization method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the multi-domain computing resource collaborative deployment optimization method according to any one of claims 1 to 5.

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